RetinaNet | Standard RetinaNet implemented with Pure PyTorch | Computer Vision library
kandi X-RAY | RetinaNet Summary
kandi X-RAY | RetinaNet Summary
Standard RetinaNet implemented with Pure PyTorch.
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- Create a function that computes the minibatch layer
- Assign a bounding box to the given boxes
- Flips the boxes of the image
- Generate a 2d mesh grid
- Calculate the image scale
- Normalize image
- Wrapper for python evaluation
- Evaluate the annotations of detections on detections
- Calculate the vocab
- Return a list of dictionaries
- Calculate the smoothed loss
- Smooth l1 loss
- Train the model
- Calculate learning rate
- Assigns the bounding boxes to the given boxes
- Forward the backbone function
- Convert detpath to list of image
- Show an image
- Evaluate bbox
RetinaNet Key Features
RetinaNet Examples and Code Snippets
Lines of Code : 6
License : No License
Copy@article{yolov3,
title={YOLOv3: An Incremental Improvement},
author={Redmon, Joseph and Farhadi, Ali},
journal = {arXiv},
year={2018}
}
Community Discussions
Trending Discussions on RetinaNet
QUESTION
I have a dataframe which has 4 columns- Name, Size, Text Extracted, Score. The score column contains a list with nan in it something like this
...ANSWER
Answered 2022-Mar-30 at 02:00From the look of it, it seems that your score column has usually a numerical result, but sometimes has a string containing "[nan nan nan ...]" rather than a list of nan.
One simple way to clean this up (here assuming an original DataFrame called df) is:
QUESTION
Despite changing the classes line to
...ANSWER
Answered 2022-Feb-15 at 10:42I figured it out and it was me being dumb
So let my dumbness provide an answer for anyone else stuck up this particular creek.
So in addition to adding
QUESTION
I am trying to resume training monkAI pytorch retinanet. I have loaded with .pt file instead of actual model. The changes are made in Monk_Object_Detection/5_pytorch_retinanet/lib/train_detector.py, check for '# change' in the places where its modified.
...ANSWER
Answered 2021-Dec-22 at 22:43I found this by simply googling your problem:
retinanet.load_state_dict(torch.load('filename').module.state_dict())
The link to the discussion is here.
QUESTION
I'm trying to implement multiprocesssing for image processing from multiple folders, when I use multiprocessing library I'm trying to pass multiple arguments to the pool function but Im getting type error, my code is as below
...ANSWER
Answered 2021-Sep-29 at 09:39Break down your processing function to work on a single image, then pass the collection of images to the process pool. You can set additional constant parameters by using functools.partial:
QUESTION
To use any of the Object Detection models from TensorFlow's Official Models in the ModelZoo, there is a variable called "VAL_JSON_FILE", which is used for the params_override argument. For my use case, I am performing transfer learning on RetinaNet. The command and arguments are found below:
ANSWER
Answered 2021-Mar-31 at 19:38https://gregsdennis.github.io/Manatee.Json/usage/schema/validation.html
This link is somewhat relevant and can provide you with more info on JSON validation. It seems to me like it's a testing (validation) of the JSON objects; checking whether it matches types.
Have you tried to run the learning without that file? I'm not certain but it could be an optional file, or there is a default one already without necessary changes needed.
QUESTION
I have a previously exported a RetinaNet model (originally from the object detection zoo) that has been fine tuned on a custom dataset with the Tensorflow Object Detection API (Tensorflow version 2.4.1). Below is how the exported model's folder looks.
When running the evaluation (like below) on the model it has a mAP@0.5IOU of 0.5.
python model_main_tf2.py --model_dir=exported-models/retinanet --pipeline_config_path=exported-models/retinanet/pipeline.config --checkpoint_dir=exported-models/retinanet/checkpoint
Due to unfortunate circumstances, I do not have the training folder from when the model was trained. As I recently got more data, I would like to use the exported model as a starting point for further training and have set the fine_tune_checkpoint: "exported-models/retinanet/checkpoint/ckpt-0" in the pipeline.config for the new training:
ANSWER
Answered 2021-Mar-17 at 15:09I finally found the following here:
QUESTION
I've been reading up a bit on different CNNs for object detection, and have found that most of the models I'm looking at are fully convolutional networks, like the latest YOLO versions and retinanet.
What are the benefits of FCNs over conventional CNNs with pooling, apart from FCNs having less different layers? I've read https://arxiv.org/pdf/1412.6806.pdf and as I read it the main interest of that paper was to simplify the networks structure. Is this the sole reason that modern detection/classification networks don't use pooling, or are there other benefits?
...ANSWER
Answered 2021-Feb-18 at 10:47With FCNs we avoid the use of dense layers, which means less parameters and because of that we can make the network learn faster.
If you avoid pooling, your output will be of the same height/width of your input. But our goal is to reduce the size of the convolutions because it is much more computationally efficient. Also, with pooling we can go deeper, as we go through higher layers individual neurons “see” more of the input. In addition, it helps to propagate information across different scales.
Usually those networks consists of a down-sampling path to extract all the necessary features and an up-sampling path to reconstruct high-level features back to the original image dimensions.
There are some architecture like "The all convolutional net" by. Springenberg, that avoids in a sense pooling in favor of speed and simplicity. In this paper the author replaced all pooling operations with stride-2 convolutions and used a global average pooling at the output layer. The global averaging pooling operation reduce the dimension of the given input.
QUESTION
I'm trying to do a simple save of a resnet50 model and I'm getting an error. My code to reproduce the error:
...ANSWER
Answered 2021-Feb-16 at 21:55You need to save the model with a format, e.g. h5. I reproduced your error, fixed it with:
resnet.save("mymodel.h5")
QUESTION
Sorry for the very basic question (I'm new with Keras). I was wondering how Keras can calculate for each layer the number of parameters at an early stage (before fit) despite that model.summary shows that there are dimensions that still have None values at this stage. Are these values already determined in some way and if yes, why not show them in the summary?
I ask the question because I'm having a hard time figure out my "tensor shape bug" (I'm trying to determine the output dimensions of the the C5 block of my resnet50 model but I cannot see them in model.summary even if I see the number of parameters).
I give below an example based on C5_reduced layer in RetinaNet which is fed by C5 layer of Resnet50. The C5_reduced is
...ANSWER
Answered 2021-Jan-29 at 20:03You need to define an input layer for your model. The total number of trainable parameters is unknown until you either a) compile the model and feed it data, at which point the model makes a graph based on the dimensions of the input and you will then be able to determine the number of params, or b) you define an input layer for the model with the input dimensions stated, then you can find the number of params with model.summary().
The point is that the model cannot know the number of parameters between the input and first hidden layer until it is defined, or you run inference and give it the shape of the input.
QUESTION
I've trained Retinanet for object detection in google colab and now I want to load its .pt file in another python project but I keep getting this error. Any thoughts?
ANSWER
Answered 2020-Dec-14 at 07:44Try these steps though:
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Install RetinaNet
You can use RetinaNet like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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